From d7dc70746ff7b5b8c6e79c4b376cefbd5c69c4b6 Mon Sep 17 00:00:00 2001 From: d3ffe1dda057aedb6d37daa14d4dce86 Date: Tue, 21 Apr 2020 21:32:25 +0000 Subject: [PATCH] question 2 --- module3/exo3/exercice.ipynb | 307 ++++++++++++++++++++++++++++++++---- 1 file changed, 280 insertions(+), 27 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 7f55b13..52c7154 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -627,20 +627,31 @@ "metadata": {}, "outputs": [], "source": [ + "total_smoker = 0\n", + "total_non_smoker = 0\n", + "total_alive = 0\n", + "total_dead = 0\n", + "\n", "alive_and_smoker = 0\n", "alive_and_non_smoker = 0\n", "dead_and_smoker = 0\n", "dead_and_non_smoker = 0\n", "for i in range(len(raw_data)):\n", " if raw_data.iloc[i][0] == \"Yes\":\n", + " total_smoker += 1\n", " if raw_data.iloc[i][1] == \"Alive\":\n", + " total_alive +=1\n", " alive_and_smoker += 1\n", " else :\n", + " total_dead +=1\n", " dead_and_smoker += 1\n", " else :\n", + " total_non_smoker += 1\n", " if raw_data.iloc[i][1] == \"Alive\":\n", + " total_alive +=1\n", " alive_and_non_smoker += 1\n", " else :\n", + " total_dead +=1\n", " dead_and_non_smoker += 1" ] }, @@ -679,6 +690,7 @@ " \n", " Smoker\n", " Non-Smoker\n", + " Total\n", " \n", " \n", " \n", @@ -686,20 +698,29 @@ " Alive\n", " 443\n", " 502\n", + " 945\n", " \n", " \n", " Dead\n", " 139\n", " 230\n", + " 369\n", + " \n", + " \n", + " Total\n", + " 582\n", + " 732\n", + " 1314\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Smoker Non-Smoker\n", - "Alive 443 502\n", - "Dead 139 230" + " Smoker Non-Smoker Total\n", + "Alive 443 502 945\n", + "Dead 139 230 369\n", + "Total 582 732 1314" ] }, "execution_count": 7, @@ -708,9 +729,9 @@ } ], "source": [ - "data = [[alive_and_smoker,alive_and_non_smoker],[dead_and_smoker, dead_and_non_smoker]]\n", + "data = [[alive_and_smoker,alive_and_non_smoker,total_alive],[dead_and_smoker, dead_and_non_smoker,total_dead], [total_smoker,total_non_smoker,(total_alive+total_dead)]]\n", "\n", - "pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\"], index = [\"Alive\", \"Dead\"])" + "pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\", \"Total\"], index = [\"Alive\", \"Dead\",\"Total\"])" ] }, { @@ -724,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -741,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -750,13 +771,13 @@ "Text(0,0.5,'Mortality Rate')" ] }, - "execution_count": 9, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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JO9o2VyY5ei37/s0klyV5RHudDya5tLUd2fq8Isknk5wNfK7z29c8SbIoydeTvC/JyiSfS7J1kv2SXJxkeZIzk/xa639Bkj9L8tUkVyV56hz7fX2Sr7XtP97a3ppkSXuNa5O8IMkp7XN7XpLNW79nts/mle2zuuUa+9669X91Wz6m1XN5kr9ZHTRJbknytiSXAAdN8Z/x/q+qfPhY6wM4CnjfyPKDgWuB32/L7wKWAw8CFgDfH9nu88CmwM7A9cAuwNOBc4AnA8uAha3/24Fj2vMdgKuAbYFXADcCD5nvfwsfXT93i4A7gP3a8ieAY9pn7Wmt7W3A/2rPLwD+vD0/DPiHOfa7CtiyPd+h/Xwr8M/A5sC+wC+AQ9u6M4H/AGwF3AA8prV/GHhDe35tq/cfgJe3tt8AzgY2b8vvHVlXwO/M97/x/eHhyEfjXAk8q/1V+dSq+mlrP2tk/SVVdXNV/QC4NckOwFOAj1XVnVX1b8CXgd9s2/wGw2Wth1fV9a3tOcCJSS5n+EWyFbCwrft8Vf1oiu9R90/frqrL2/NlwCMZAuPLrW0J8Nsj/T890nfRHPtcDvyfJMcwhNtq51bV7Qyf502B81r7lW1fe7V6rprjtT8DnF5VH27LzwT2By5tn+lnAo9o6+4Ezpj7bW88PI6uOVXVVUn2Z/hr8k+SrD70dVv7edfI89XLmwEZs9vvMoTLExj+EqX1P6qqvjnaMcmTgJ/fpzehB6rRz9WdDCPiSfrfSfu9luR02uesqg4DnscQGkcAb0myz+i2VXVXkturDVGY7PMMcCFwaJK/bdsGWFJVb1pL31ur6s517G+j4MhHc0qyK/CLqvoo8E7giRNu+o/A0Uk2TbKA4T/8V9u6nzD8Enh7kqe3tvOB1yVJe90nbKC3oNnxU+DHI+dzXsYwop5TVR1XVftV1WFJNgF2r6ovAScwhNl2E772N4BFSR41x2v/EfBDhsNrAF8AXpjkoQBJHpJkjwlfa6Nh+GicxwFfbYcO3gz8jwm3O5PhEMcVwBeBE6rqe6tXtkNxhwPvaaObkxmOuS9PsqItS2s6FnhHkuXAfgznfSa1KfDRJFcClwHvqqqfTLJhVd0KHAd8sm1/F3DqGt3eAGyV5JSq+hrw34HPtVo/z3DOUyOc4UCS1J0jH0lSd4aPJKk7w0eS1J3hI0nqzvCRJHVn+EhTluTNbY6y5W2uryfdx/2tdXbwJEckOfG+7FvqxRkOpClKchDwfOCJVXVbm35/i2m8VlWdxT1TH0n3a458pOnaBbipqlZP4XJTVa2a0uzgr0jyV639Q0neneQrSa5J8sLWvkmS97aR2DlJ/n71Oqknw0ears8Bu7ep/t+b5Gkj626oqoOAfwI+BLwQOJB7vrn/AoZv8u8LPIvh2/13f1M+yZMZvml/ZFVds5bX3oVhktfnA386ss9FDLNX/B4b+7T+mjcedpOmqKpuaZOzPhV4BvB/R87LjM4Ovl1V3QzcnORXZgcH/i3J6tnBf8Y9s4M/p6pWsXZ/V1V3AV9LsnNrewrwydb+vSRf2rDvWJqM4SNNWQuPC4AL2txgx7ZVG3p28DWN7jNr/JTmlYfdpClKsleSR4807QdcN+Hm93Z28En8M3BUO/ezM8MN/qTuHPlI07Ud8L/bYbQ7gKuB4xnOw6zLmQznZK5guAPmCVX1vSS/DsPs4EkOB85N8soJ6zmD4eZmKxjuGHsJw+0KpK6c1VrayCTZrp2L2pFhJPVbo7e8kHpw5CNtfM5pI7EtgJMNHs0HRz6SpO684ECS1J3hI0nqzvCRJHVn+EiSujN8JEndGT6SpO7+P1C47Up+gYJ4AAAAAElFTkSuQmCC\n", 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" ] @@ -770,7 +791,7 @@ "source": [ "mortality_rate = [mortality_rate_smoker,mortality_rate_non_smoker]\n", "smoking = ['smoker', 'non-smoker']\n", - "plt.bar(smoking, mortality_rate,color=['blue', 'green'])\n", + "plt.bar(smoking, mortality_rate,color=['#E69F00', '#56B4E9'],width = 0.5)\n", "plt.xticks(smoking)\n", "plt.yticks(mortality_rate)\n", "plt.xlabel('Smoking')\n", @@ -800,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -825,39 +846,271 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nous allons reprendre les calculs d'effectif et de taux de mortalite calcules precedemment, mais nous allons les categoriser par tranche d'age. Les femmes ayant participe a ces etudes seront reparties dans quatre categories en fonction de leur age : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans." + "Nous allons reprendre les calculs d'effectif et de taux de mortalite calcules precedemment, mais nous allons les categoriser par tranche d'age. Les femmes ayant participe a ces etudes seront reparties dans quatre categories en fonction de leur age : 18-35 ans, 35-55 ans, 55-64 ans, plus de 65 ans." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class_18_to_35 = []\n", + "class_35_to_55 = []\n", + "class_55_to_64 = []\n", + "class_over_65 = []\n", + "for i in range(len(raw_data)):\n", + " if raw_data.iloc[i][2] < 35:\n", + " class_18_to_35.append(raw_data.iloc[i])\n", + " elif 35 <= raw_data.iloc[i][2] < 55:\n", + " class_35_to_55.append(raw_data.iloc[i])\n", + " elif 55 <= raw_data.iloc[i][2] < 65 :\n", + " class_55_to_64.append(raw_data.iloc[i])\n", + " else :\n", + " class_over_65.append(raw_data.iloc[i])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, + "outputs": [], + "source": [ + "alive_and_smoker_18to35 = 0\n", + "alive_and_non_smoker_18to35 = 0\n", + "dead_and_smoker_18to35 = 0\n", + "dead_and_non_smoker_18to35 = 0\n", + "for i in range(len(class_18_to_35)):\n", + " if class_18_to_35[i][0] == \"Yes\":\n", + " if class_18_to_35[i][1] == \"Alive\":\n", + " alive_and_smoker_18to35 += 1\n", + " else :\n", + " dead_and_smoker_18to35 += 1\n", + " else :\n", + " if class_18_to_35[i][1] == \"Alive\":\n", + " alive_and_non_smoker_18to35 += 1\n", + " else :\n", + " dead_and_non_smoker_18to35 += 1\n", + "\n", + "alive_and_smoker_35to55 = 0\n", + "alive_and_non_smoker_35to55 = 0\n", + "dead_and_smoker_35to55 = 0\n", + "dead_and_non_smoker_35to55 = 0\n", + "for i in range(len(class_35_to_55)):\n", + " if class_35_to_55[i][0] == \"Yes\":\n", + " if class_35_to_55[i][1] == \"Alive\":\n", + " alive_and_smoker_35to55 += 1\n", + " else :\n", + " dead_and_smoker_35to55 += 1\n", + " else :\n", + " if class_35_to_55[i][1] == \"Alive\":\n", + " alive_and_non_smoker_35to55 += 1\n", + " else :\n", + " dead_and_non_smoker_35to55 += 1\n", + "\n", + "alive_and_smoker_55to64 = 0\n", + "alive_and_non_smoker_55to64 = 0\n", + "dead_and_smoker_55to64 = 0\n", + "dead_and_non_smoker_55to64 = 0\n", + "for i in range(len(class_55_to_64)):\n", + " if class_55_to_64[i][0] == \"Yes\":\n", + " if class_55_to_64[i][1] == \"Alive\":\n", + " alive_and_smoker_55to64 += 1\n", + " else :\n", + " dead_and_smoker_55to64 += 1\n", + " else :\n", + " if class_55_to_64[i][1] == \"Alive\":\n", + " alive_and_non_smoker_55to64 += 1\n", + " else :\n", + " dead_and_non_smoker_55to64 += 1\n", + "\n", + "alive_and_smoker_over65 = 0\n", + "alive_and_non_smoker_over65 = 0\n", + "dead_and_smoker_over65 = 0\n", + "dead_and_non_smoker_over65 = 0\n", + "for i in range(len(class_over_65)):\n", + " if class_over_65[i][0] == \"Yes\":\n", + " if class_over_65[i][1] == \"Alive\":\n", + " alive_and_smoker_over65 += 1\n", + " else :\n", + " dead_and_smoker_over65 += 1\n", + " else :\n", + " if class_over_65[i][1] == \"Alive\":\n", + " alive_and_non_smoker_over65 += 1\n", + " else :\n", + " dead_and_non_smoker_over65 += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Smoker [18-35]Non-Smoker [18-35]Smoker [35-55]Non-Smoker [35-55]Smoker [55-64]Non-Smoker [55-64]Smoker [65+]Non-Smoker [65+]
Alive1822211901726481728
Dead763919514042165
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" + ], + "text/plain": [ + " Smoker [18-35] Non-Smoker [18-35] Smoker [35-55] Non-Smoker [35-55] \\\n", + "Alive 182 221 190 172 \n", + "Dead 7 6 39 19 \n", + "\n", + " Smoker [55-64] Non-Smoker [55-64] Smoker [65+] Non-Smoker [65+] \n", + "Alive 64 81 7 28 \n", + "Dead 51 40 42 165 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_18to35 = [[alive_and_smoker_18to35,alive_and_non_smoker_18to35],[dead_and_smoker_18to35, dead_and_non_smoker_18to35]]\n", + "data_35to55 = [[alive_and_smoker_35to55,alive_and_non_smoker_35to55],[dead_and_smoker_35to55, dead_and_non_smoker_35to55]]\n", + "data_55to64 = [[alive_and_smoker_55to64,alive_and_non_smoker_55to64],[dead_and_smoker_55to64, dead_and_non_smoker_55to64]]\n", + "data_over65 = [[alive_and_smoker_over65,alive_and_non_smoker_over65],[dead_and_smoker_over65, dead_and_non_smoker_over65]]\n", + "\n", + "df1 = pd.DataFrame(data_18to35, columns=[\"Smoker [18-35]\", \"Non-Smoker [18-35]\"], index = [\"Alive\", \"Dead\"])\n", + "df2 = pd.DataFrame(data_35to55, columns=[\"Smoker [35-55]\", \"Non-Smoker [35-55]\"], index = [\"Alive\", \"Dead\"])\n", + "df3 = pd.DataFrame(data_55to64, columns=[\"Smoker [55-64]\", \"Non-Smoker [55-64]\"], index = [\"Alive\", \"Dead\"])\n", + "df4 = pd.DataFrame(data_over65, columns=[\"Smoker [65+]\", \"Non-Smoker [65+]\"], index = [\"Alive\", \"Dead\"])\n", + "\n", + "df_total = pd.concat([df1,df2,df3,df4],axis=1)\n", + "df_total" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "398 438 236 242\n" + "0.037037037037037035 0.02643171806167401\n", + "0.1703056768558952 0.09947643979057591\n", + "0.4434782608695652 0.3305785123966942\n", + "0.8571428571428571 0.8549222797927462\n" ] } ], "source": [ - "class_18_to_34 = []\n", - "class_34_to_54 = []\n", - "class_55_to_64 = []\n", - "class_over_65 = []\n", - "for i in range(len(raw_data)):\n", - " if raw_data.iloc[i][2] < 34:\n", - " class_18_to_34.append(raw_data.iloc[i])\n", - " elif 34 <= raw_data.iloc[i][2] < 55:\n", - " class_34_to_54.append(raw_data.iloc[i])\n", - " elif 55 <= raw_data.iloc[i][2] < 65 :\n", - " class_55_to_64.append(raw_data.iloc[i])\n", - " else :\n", - " class_over_65.append(raw_data.iloc[i])\n", - "print(len(class_18_to_34),len(class_34_to_54),len(class_55_to_64),len(class_over_65))" + "mortality_rate_smoker_18to35 = dead_and_smoker_18to35/(alive_and_smoker_18to35 + dead_and_smoker_18to35)\n", + "mortality_rate_non_smoker_18to35 = dead_and_non_smoker_18to35/(alive_and_non_smoker_18to35 + dead_and_non_smoker_18to35)\n", + "\n", + "mortality_rate_smoker_35to55 = dead_and_smoker_35to55/(alive_and_smoker_35to55 + dead_and_smoker_35to55)\n", + "mortality_rate_non_smoker_35to55 = dead_and_non_smoker_35to55/(alive_and_non_smoker_35to55 + dead_and_non_smoker_35to55)\n", + "\n", + "mortality_rate_smoker_55to64 = dead_and_smoker_55to64/(alive_and_smoker_55to64 + dead_and_smoker_55to64)\n", + "mortality_rate_non_smoker_55to64 = dead_and_non_smoker_55to64/(alive_and_non_smoker_55to64 + dead_and_non_smoker_55to64)\n", + "\n", + "mortality_rate_smoker_over65 = dead_and_smoker_over65/(alive_and_smoker_over65 + dead_and_smoker_over65)\n", + "mortality_rate_non_smoker_over65 = dead_and_non_smoker_over65/(alive_and_non_smoker_over65 + dead_and_non_smoker_over65)\n", + "\n", + "print(mortality_rate_smoker_18to35,mortality_rate_non_smoker_18to35)\n", + "print(mortality_rate_smoker_35to55,mortality_rate_non_smoker_35to55)\n", + "print(mortality_rate_smoker_55to64,mortality_rate_non_smoker_55to64)\n", + "print(mortality_rate_smoker_over65,mortality_rate_non_smoker_over65)\n" ] }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mlkEOdzOzDEoV7pJGSForqU7StCL7h0naKqk2eX2r9KWamVlaLT6sQ1IF8ABwIblnpdZImh8RqwuaPh8R/6sdajQzs1ZKM3IfAtRFxLqI2AHMAUa1b1lmZrYn0oR7L2B93np9sq3QUEnLJf1S0ieLHUjSRElLJS1taGhoQ7lmZpZGmnBXkW1RsP4KcGxEnAR8H/h5sQNFxMyIqI6I6srKytZVamZmqaUJ93qgT956b2BDfoOIeCsitiXLC4H9JPUoWZVmZtYqacK9BugvqUrS/sBYYH5+A0lHS1KyPCQ57uZSF2tmZum0eLVMRDRKmgw8DVQAsyJilaRJyf4ZwL8A/y6pEfg7MDYiCk/dmJlZB2kx3KHpVMvCgm0z8pbvB+4vbWlmZtZWvkPVzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZVCqm5jMrGUbZxebY6/9HDPBN4Fb8zxyNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llUKpwlzRC0lpJdZKm7abdqZJ2SvqX0pVoZmat1WK4S6oAHgBGAgOBcZIGNtPuLnJPbDIzszJKM3IfAtRFxLqI2AHMAUYVaTcFeAJ4o4T1mZlZG6QJ917A+rz1+mRbE0m9gMuBGeyGpImSlkpa2tDQ0NpazcwspTThXuye6sL7nv8D+FpE7NzdgSJiZkRUR0R1ZWVl2hrNzKyV0swtUw/0yVvvDWwoaFMNzJEE0AO4WFJjRPy8JFWamVmrpAn3GqC/pCrgf4CxwL/mN4iIqg+WJc0GFjjYzczKp8Vwj4hGSZPJXQVTAcyKiFWSJiX7d3ue3czMOl6qKX8jYiGwsGBb0VCPiAl7XpaZme0J36FqZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZ5HA3M8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWVQqrllzKzzGfNkxz707PFLjuzQz7M945G7mVkGOdzNzDLI4W5mlkGpwl3SCElrJdVJmlZk/yhJKyTVJg/APrP0pZqZWVot/kFVUgXwAHAhueep1kiaHxGr85r9GpgfESFpMPBj4IT2KNjMzFqWZuQ+BKiLiHURsQOYA4zKbxAR2yIiktWDgMDMzMomTbj3Atbnrdcn2z5E0uWSXgWeBK4tdiBJE5PTNksbGhraUq+ZmaWQJtxVZNsuI/OI+FlEnABcBny72IEiYmZEVEdEdWVlZesqNTOz1NKEez3QJ2+9N7ChucYR8RzwMUk99rA2MzNrozThXgP0l1QlaX9gLDA/v4Gkj0tSsvwpYH9gc6mLNTOzdFq8WiYiGiVNBp4GKoBZEbFK0qRk/wzgn4Hxkt4D/g6MyfsDq5mZdbBUc8tExEJgYcG2GXnLdwF3lbY0MzNrK9+hamaWQQ53M7MMcribmWWQw93MLIMc7mZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczyyCHu5lZBqUKd0kjJK2VVCdpWpH9V0pakbx+K+mk0pdqZmZptRjukiqAB4CRwEBgnKSBBc3+BJwTEYPJPRx7ZqkLNTOz9NKM3IcAdRGxLiJ2AHOAUfkNIuK3EfG3ZPVFcg/RNjOzMkkT7r2A9Xnr9cm25nwO+GWxHZImSloqaWlDQ0P6Ks3MrFXShLuKbCv68GtJ55IL968V2x8RMyOiOiKqKysr01dpZmatkuYB2fVAn7z13sCGwkaSBgM/AEZGxObSlGdmZm2RZuReA/SXVCVpf2AsMD+/gaS+wFzgqoj4Q+nLNDOz1mhx5B4RjZImA08DFcCsiFglaVKyfwbwLeAI4D8lATRGRHX7lW1mZruT5rQMEbEQWFiwbUbe8ueBz5e2NDMzayvfoWpmlkEOdzOzDEp1Wsaya+PsYle6tp9jJhS9itbMSswjdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZlCrcJY2QtFZSnaRpRfafIGmJpHclfbn0ZZqZWWu0OCukpArgAeBCcs9TrZE0PyJW5zV7E5gKXNYuVZqZWaukGbkPAeoiYl1E7ADmAKPyG0TEGxFRA7zXDjWamVkrpQn3XsD6vPX6ZJuZmXVSaR7WUexpDm164oKkicBEgL59+7blELaXG/PkGx36eY9fcmSHfp5ZZ5Fm5F4P9Mlb7w1saMuHRcTMiKiOiOrKysq2HMLMzFJIE+41QH9JVZL2B8YC89u3LDMz2xMtnpaJiEZJk4GngQpgVkSskjQp2T9D0tHAUqA78L6k64GBEfFWO9ZuZmbNSPWA7IhYCCws2DYjb/l1cqdrzMysE/AdqmZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczy6BU0w90NhtnF5uFuP0cM6FNMxybmZWNR+5mZhm0V47cO5ofMGFmexuP3M3MMsjhbmaWQQ53M7MMShXukkZIWiupTtK0Ivsl6b5k/wpJnyp9qWZmllaL4S6pAngAGAkMBMZJGljQbCTQP3lNBP5Pies0M7NWSDNyHwLURcS6iNgBzAFGFbQZBfwwcl4EDpV0TIlrNTOzlNJcCtkLWJ+3Xg+clqJNL2BjfiNJE8mN7AG2SVrbqmrL5qi2vrEHsKm1b/pxWz9tr+C+LB33ZensVX15bJpGacK92O2ghbdspmlDRMwEZqb4zEyQtDQiqstdRxa4L0vHfVk6nbkv05yWqQf65K33Bja0oY2ZmXWQNOFeA/SXVCVpf2AsML+gzXxgfHLVzOnA1ojYWHggMzPrGC2elomIRkmTgaeBCmBWRKySNCnZPwNYCFwM1AHvANe0X8l7lX3mFFQHcF+WjvuydDptXyrCMx6amWWN71A1M8sgh7uZWQY53FOQNEvSG5JW5m07WdKLkmolLZU0pJn3fjuZkqFW0iJJPZPt/ST9PdleK2lGR32fcpLUTdLvJC2XtErSrcn2WyT9T15/XNzM+4u224f788+Sfv/B72GyLVVfJm2nJFOLrJL0nYJ9fSVtk/Tl9v4enZGkQyX9VNKrktZIGtqavi03n3NPQdLZwDZyd+GemGxbBNwbEb9MfsBfjYhhRd7bPSLeSpanAgMjYpKkfsCCD463r5Ak4KCI2CZpP2Ax8EVgBLAtIqa38P5birXbh/vzz0B1RGzK23YL6fryXOBm4JKIeFfSkRHxRt7+J4D3gZdaOlYWSXoEeD4ifpBcKXggcD0t9G3S/3+OiNkdUmgz/LCOFCLiuSQ8PrQZ6J4sH0Iz1/V/EOyJgyhyc9e+JHKjiW3J6n7Ja5/ukzL6d+DOiHgXoCDYLwPWAW+XqbayktQdOBuYAJBMvbIjNzbZO/i0TNtdD9wtaT0wHfh6cw0l3ZG0uxL4Vt6uKknLJP1G0lntW27nIalCUi3wBvDfEfFSsmtycgprlqTDdnOI5trti/0ZwCJJLyfTe3wgTV8OAM6S9FLSZ6cCSDoI+Bpwa/uW3qkdBzQADye/Uz9I+gXS/56WV0T4leIF9ANW5q3fB/xzsnwF8KsUx/g6cGuyfABwRLL8aXJz83Qv9/fs4D49FHgGOJHc5B4V5AYcd5C7n6LYe4q221f7E+iZ/PNIYDm50WbavlyZ/B6L3ASBf0qWpwNXJG1uAb5c7u9Zhn6tBhqB05L17wHf3s3v3yCgNnm9DryWt35EWb5DuTtxb3kVCfet/ONvFgLeSpYfTn6gC4sc49j8YxTse5bcudOyf9cO7tf/XRge+X3dQn/2c39+6DvvEsS760vgKWBYXtv/C1QCzwN/Tl5bgDeByeX+fh3cl0eTO2/+wfpZwJPN9W2Rn8OEcn8Hn3Nvuw3AOeRC5DzgjwAR8aG7cyX1j4g/JqufBV5NtlcCb0bETknHkZsLf13HlF4+yfd+LyK2SPoIcAFwl6Rj4h9TVlxOblRZrD+LttsX+zM5TdAlIv5fsnwRcFvavgR+Tu5391lJA4D9gU0R0XRKK++Ps/e377fpXCLidUnrJR0fEWuB84HVzfVtZ+RwT0HSfwHDgB6S6smNNv8N+J6krsB2/jGVcaE7JR1P7qqDvwCTku1nk/sXsRHYCUyKiDfb71t0GscAjyj3EJguwI8jYoGkH0k6mdw55D8D1zXz/u80025f7M+jgJ8lf+TrCjwWEU+1oi9nAbOUu8R3B3B1JENPA2AK8Ghypcw6ctOq3Jeyb8vOl0KamWWQr5YxM8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWWQw93MLIP+P0fdiOEAK6slAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mortality_rate_smoker = (mortality_rate_smoker_18to35,mortality_rate_smoker_35to55,mortality_rate_smoker_55to64,mortality_rate_smoker_over65)\n", + "mortality_rate_non_smoker = (mortality_rate_non_smoker_18to35,mortality_rate_non_smoker_35to55,mortality_rate_non_smoker_55to64,mortality_rate_non_smoker_over65)\n", + "age = ['18-35','35-55','55-64','65+']\n", + "indices = range(len(mortality_rate_smoker))\n", + "width = np.min(np.diff(indices))/3.\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.bar(indices-width/2.,mortality_rate_smoker,width,color='#E69F00',label='Smoker')\n", + "ax.bar(indices+width/2.,mortality_rate_non_smoker,width,color='#56B4E9',label='Non-Smoker')\n", + "#tiks = ax.get_xticks().tolist()\n", + "plt.xticks(indices + width / 2, age)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, -- 2.18.1